Metadata
- URL: https://locallyoptimistic.com/post/building-more-effective-data-teams-using-the-jtbd-framework/
- Published Date: 2022-06-20
- Author: Emilie Schario
- Tags:: Data culture, Data team vision and mission, Measuring a data team impact
Highlights
The way your team drives impact to the business is through improved decision-making- both for your company and your customers. While many people can conceptualize that, and rarely do I get any push back on the idea, it can be hard to take that mission and put it into practice. I often get asked: So, how exactly does my data team help drive improved decision-making?
The five jobs to be done by modern data teams are: 1. data activation – making operational data available to the teams that need it 2. metrics management – the business needs shared definitions and a baseline of key metrics 3. proactive insight discovery – team members outside of the data team are limited in the questions they can ask by their limited knowledge of what data exists and what questions can be asked 4. driving experimentation – driving measurable impact to the business through A/B experimentation moving key business metrics in the right direction 5. interfacing with the data – empowering team members across the business with the information and conclusions they need to be unblocked
In a data team-led insight discovery model though, the data team recognizes that these sorts of questions- questions that can only be answered with advanced analysis- must be driven by the data team in collaboration with other parts of the organization.
Instead of limiting the knowledge sharing to the stakeholder who asked the question, the data team is responsible for scaling knowledge within an organization.
Many data teams aren’t involved with experimentation at all. Or, if they are, they are only running analyses for experiments, explaining statistical significance and p-values, or, disappointingly, explaining how an infrastructure glitch led to a poor rollout. It takes many attempts to be able to successfully run an experiment. Even once you get there, they can be very resource-demanding from a time perspective.
Moreover, Kohavi et. al (2019) find that “only about 1/3 of ideas improve the metrics they were designed to improve”. Companies are shipping things that don’t matter and they can use experimentation to help identify those more quickly.